[https://nvbugs/5519525][fix] fix doc invalid link for bug 5519525

[nv-guomingz]

Summary by CodeRabbit

• Documentation
  • Updated links to the TensorRT-Model-Optimizer deployment docs.
  • Expanded PyTorch quantization guidance with a sample hf_quant_config.json, KV-cache quantization behavior, mapping table, and examples.
  • Added instructions to force KV-cache dtype via YAML (kv_cache_dtype) and documented valid values (auto, fp8).
  • Clarified phrasing and added example YAML snippets and notes to aid configuration.

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

Updated perf-benchmarking documentation: replaced two TensorRT Model Optimizer links and expanded the PyTorch quantization section with hf_quant_config.json schema, kv_cache_quant_algo behavior, trtllm-bench KV-cache-selection logic, a behavior matrix, and a YAML kv_cache_dtype override example plus valid values.

Changes

Cohort / File(s)	Summary
Docs: Perf benchmarking and quantization docs/source/developer-guide/perf-benchmarking.md	Replaced two TensorRT Model Optimizer URLs with the TensorRT-LLM deployment page; expanded PyTorch flow quantization guidance including sample hf_quant_config.json structure, kv_cache_quant_algo, and how trtllm-bench chooses KV-cache quantization; added mapping of compute vs KV-cache decisions, YAML kv_cache_dtype override example and valid values (auto, fp8); minor wording edits.

Sequence Diagram(s)

sequenceDiagram
  autonumber
  participant U as User
  participant TB as trtllm-bench
  participant CKPT as Checkpoint (hf_quant_config.json)
  participant CFG as YAML Config

  Note over U,TB: Run benchmark in PyTorch flow
  U->>TB: Invoke benchmark
  TB->>CKPT: Read hf_quant_config.json
  CKPT-->>TB: Return compute_quant, kv_cache_quant_algo
  TB->>CFG: Read kv_cache_dtype (if present)
  alt YAML kv_cache_dtype provided
    TB->>TB: Force KV-cache dtype per YAML (auto | fp8)
  else No YAML override
    TB->>TB: Determine KV-cache quant using compute_quant + kv_cache_quant_algo rules
  end
  TB-->>U: Start benchmark with selected compute & KV-cache quantization

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Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description Check	⚠️ Warning	The PR body is just the repository template and placeholder text without a filled "Description" or "Test Coverage" and therefore does not explain what was changed, why, or how it was verified; it lacks the concrete details (files changed, the exact broken link, verification steps) reviewers need to validate the fix. Although the PR metadata and raw_summary indicate a documentation link was corrected for NVBugs 5519525, that context is not present in the PR description itself, making the description incomplete for review. This omission also prevents automated reviewers or maintainers from quickly assessing test impact and whether CI can be skipped.	Please update the PR description by replacing the template placeholders with a concise Description that lists the modified file(s) and the exact link fixed (including the NVBugs ID), a Test Coverage section describing how the change was verified, and any CI notes (e.g., "docs only change" / bot command already used); also confirm the PR checklist items relevant to docs updates.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title Check	✅ Passed	The title follows the repository ticket/type template and clearly identifies the NVBugs ID and the purpose of the change (fixing an invalid documentation link), succinctly summarizing the primary intent for reviewers. It is specific and directly related to the changes described in the PR metadata.
Docstring Coverage	✅ Passed	No functions found in the changes. Docstring coverage check skipped.

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[nv-guomingz]

/bot skip --comments "docs only change"

[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (2)

docs/source/developer-guide/perf-benchmarking.md (2)

447-449: Nit: fix punctuation in this sentence.

Change “When running trtllm-bench throughput. The benchmark will …” to a single sentence.

-When running `trtllm-bench throughput`. The benchmark will select a KV cache quantization ...
+When running `trtllm-bench throughput`, the benchmark will select a KV cache quantization ...

---

428-444: Optional: add a cross‑reference to the hf_quant_config.json schema.

After introducing hf_quant_config.json, link to the ModelOpt “Unified HuggingFace Checkpoint” page that documents this file and its fields (quantization.quant_algo, quantization.kv_cache_quant_algo). This will help readers align expectations across frameworks. (nvidia.github.io)

📜 Review details

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 7df515e and cb3b4af.

📒 Files selected for processing (1)

• docs/source/developer-guide/perf-benchmarking.md (1 hunks)

🧰 Additional context used

🧠 Learnings (3)

📚 Learning: 2025-09-09T09:40:45.658Z

Learnt from: fredricz-20070104
PR: NVIDIA/TensorRT-LLM#7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.

Applied to files:

• docs/source/developer-guide/perf-benchmarking.md

📚 Learning: 2025-08-06T13:58:07.506Z

Learnt from: galagam
PR: NVIDIA/TensorRT-LLM#6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.

Applied to files:

• docs/source/developer-guide/perf-benchmarking.md

📚 Learning: 2025-07-28T17:06:08.621Z

Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.

Applied to files:

• docs/source/developer-guide/perf-benchmarking.md

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (1)

docs/source/developer-guide/perf-benchmarking.md (1)

426-426: Good fix: updated ModelOpt doc link is valid and points to the TensorRT‑LLM deployment section.

The new URL resolves and is the right target for exporting/using quantized checkpoints with TensorRT‑LLM. (nvidia.github.io)

[tensorrt-cicd]

PR_Github #18725 Bot args parsing error: usage: /bot skip --comment COMMENT
/bot skip: error: the following arguments are required: --comment

[nv-guomingz]

/bot skip --comment "docs only change"

[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (1)

docs/source/developer-guide/perf-benchmarking.md (1)

446-449: Grammar/flow nit: fix subordinate clause sentence.

“When running trtllm-bench throughput. The benchmark …” is ungrammatical. Merge into a single sentence.

-When running `trtllm-bench throughput`. The benchmark will select a KV cache quantization that is best suited
-for the compute precision in the checkpoint automatically if `kv_cache_quant_algo` is specified as `null`, otherwise it will
-be forced to match the specified non-null KV cache quantization. The following are the mappings that `trtllm-bench` will
-follow when a checkpoint does not specify a KV cache quantization algorithm:
+When running `trtllm-bench throughput`, the benchmark automatically selects a KV‑cache quantization best suited
+to the checkpoint’s compute precision if `kv_cache_quant_algo` is `null`; otherwise, it uses the specified non‑null
+KV‑cache quantization. The following mappings apply when a checkpoint does not specify a KV‑cache quantization algorithm:

📜 Review details

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Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between cb3b4af and b53a264.

📒 Files selected for processing (1)

• docs/source/developer-guide/perf-benchmarking.md (1 hunks)

🧰 Additional context used

🧠 Learnings (3)

📚 Learning: 2025-09-09T09:40:45.658Z

Learnt from: fredricz-20070104
PR: NVIDIA/TensorRT-LLM#7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.

Applied to files:

• docs/source/developer-guide/perf-benchmarking.md

📚 Learning: 2025-08-06T13:58:07.506Z

Learnt from: galagam
PR: NVIDIA/TensorRT-LLM#6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.

Applied to files:

• docs/source/developer-guide/perf-benchmarking.md

📚 Learning: 2025-07-28T17:06:08.621Z

Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.

Applied to files:

• docs/source/developer-guide/perf-benchmarking.md

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (2)

docs/source/developer-guide/perf-benchmarking.md (2)

426-426: AI summary appears broader than the actual diff.

The AI summary mentions multiple link replacements and added quantization guidance, but the provided diff only shows this single link update. Confirm whether other intended doc edits were already merged elsewhere or should be included here.

---

426-426: Linkcheck passed — external doc URL returns 200 OK. Verified with curl: https://nvidia.github.io/TensorRT-Model-Optimizer/deployment/1_tensorrt_llm.html -> 200 (no redirect).

[tensorrt-cicd]

PR_Github #18748 [ skip ] triggered by Bot

[tensorrt-cicd]

PR_Github #18748 [ skip ] completed with state SUCCESS
Skipping testing for commit b53a264